Name
Abstract | ||
|---|---|---|
Dual control frameworks for systems subject to uncertainties aim at simultaneously learning the unknown parameters while controlling the system dynamics. We propose a robust dual model predictive control algorithm for systems with bounded uncertainty with application to soft landing control. The algorithm exploits a robust control invariant set to guarantee constraint enforcement in spite of the uncertainty, and a constrained estimation algorithm to guarantee admissible parameter estimates. The impact of the control input on parameter learning is accounted for by including in the cost function a reference input, which is designed online to provide persistent excitation. The reference input design problem is non-convex, and here is solved by a sequence of relaxed convex problems. The results of the proposed method in a soft-landing control application in transportation systems are shown. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ACC.2015.7171932 | American Control Conference |
Field | DocType | ISSN |
Mathematical optimization,Control theory,Computer science,Model predictive control,Control engineering,Robustness (computer science),System dynamics,Invariant (mathematics),Adaptive control,Soft landing,Robust control,Bounded function | Conference | 0743-1619 |
ISBN | Citations | PageRank |
978-1-4799-8685-9 | 1 | 0.37 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongfang Cheng | 1 | 2 | 1.40 |
| Haghighat, S. | 2 | 4 | 0.83 |
| S. Cairano | 3 | 249 | 26.23 |